Triple
T36320653
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Groom, Texas |
E894321
|
entity |
| Predicate | religiousLandmarkHeight |
P168569
|
FINISHED |
| Object | cross over 150 feet tall |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: cross over 150 feet tall | Statement: [Groom, Texas, religiousLandmarkHeight, cross over 150 feet tall]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: religiousLandmarkHeight Context triple: [Groom, Texas, religiousLandmarkHeight, cross over 150 feet tall]
-
A.
centralMonumentHeight
chosen
Indicates the height measurement of a central monument in a given context or location.
-
B.
highestPillarApproximateHeight
Indicates the estimated height value of the tallest pillar in a given context or structure.
-
C.
mountainHeight
Indicates the vertical elevation or height of a mountain, typically measured from sea level.
-
D.
countryRankByHeight
Indicates the relative position of a country when countries are ordered by the height of something (e.g., average elevation, tallest point, or average citizen height).
-
E.
rankByHeightWorld
Indicates an ordering of entities based on their relative height compared to all others in the world.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f76e4d1a788190a6ab6ccca28547a7 |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_69ffef812da48190b875a7376b24f92d |
completed | May 10, 2026, 2:37 a.m. |
| PD | Predicate disambiguation | batch_69ffedecd580819097851b1473fdd6ed |
completed | May 10, 2026, 2:31 a.m. |
Created at: May 3, 2026, 4:09 p.m.